Method for identifying outliers in large data sets

ABSTRACT

A new method for identifying a predetermined number of data points of interest in a large data set. The data points of interest are ranked in relation to the distance to their neighboring points. The method employs partition-based detection algorithms to partition the data points and then compute upper and lower bounds for each partition. These bounds are then used to eliminate those partitions that do contain the predetermined number of data points of interest. The data points of interest are then computed from the remaining partitions that were not eliminated. The present method eliminates a significant number of data points from consideration as the points of interest, thereby resulting in substantial savings in computational expense compared to conventional methods employed to identify such points.

FIELD OF THE INVENTION

[0001] The present invention relates to data sets, and more particularly to a method for identifying particular data points of interest in a large data set

BACKGROUND OF THE INVENTION

[0002] The ability to identify particular data points in a data set that are dissimilar from the remaining points in the set has useful applications in the scientific and financial fields. For example, identifying such dissimilar points, which are commonly referred to as outliers, can be used to identify abnormal usage patterns for a credit card to detect a stolen card. The points in the abnormal usage pattern associated with the unauthorized use of the stolen card are deemed outliers with respect to the normal usage pattern of the cardholder.

[0003] Conventional methods employed for identifying outliers typically use an algorithm which relies upon a distance-based definition for outliers in which a point p in a data set is an outlier if no more than k points in the data set are at a distance of d or less from the point p. The distance d function can be measured using any conventional metric.

[0004] Although, methods which employ the aforementioned conventional distance-based definition of outliers can be used to identify such points in large data sets, they suffer from a significant drawback. Specifically, they are computationally expensive since they identify all outliers rather than ranking and thus identifying only particular outliers that are of interest. In addition, as the size of a data set increases, conventional methods require increasing amounts of time and hardware to identify the outliers.

SUMMARY OF THE INVENTION

[0005] A new method for identifying a predetermined number of outliers of interest in a large data set. The method uses a new definition of outliers in which such points are ranked in relation to their neighboring points. The method also employs new partition-based detection algorithms to partition the data points, and then compute upper and lower bounds for each partition. These bounds are then used to identify and eliminate those partitions that cannot possibly contain the predetermined number of outliers of interest. Outliers are then computed from the remaining points residing in the partitions that were not eliminated. The present method eliminates a significant number of data points from consideration as outliers, thereby resulting in substantial savings in computational expense compared to conventional methods employed to identify such points.

BRIEF DESCRIPTION OF THE DRAWINGS

[0006]FIG. 1 shows a flow chart depicting the steps in an exemplary embodiment of a method for identifying a predetermined number of outliers of interest in a data set according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0007] The following detailed description relates to an exemplary embodiment of a method for identifying a predetermined number of outliers of interest in a large data set according to the present invention. The method utilizes both a new definition of outliers and new algorithms to identify such data points. The following terms will aid in understanding both the new definition and the new algorithms: k represents the number of neighbors of a data point p that is of interest; D^(k)(p) represents the distance of point p to its k^(th) nearest neighbor; n represents the total number of outliers of interest; N represents the total number of data points in a data set; δ represents the dimensionality of data points N; M represents the amount of memory available; MINDIST represents the minimum distance between a point or a minimum bounding rectangle (MBR) and another MBR; and MAXDIST represents the maximum distance between a point or an MBR and another MBR.

[0008] The new definition of outliers is based on determining the distance D^(k)(p) of a point p to its k^(th) nearest neighbor and then determining top n outliers, such that for a given a data set with N points and parameters n and k, an outlier can be defined as follows: a point p is a D^(k) _(d), pronounced “dee-kay-en”, outlier if no more than an n−1 other points in the data set have a higher value for D^(k) than p. Thus, the top n points with the maximum D^(k) values are deemed outliers. For example, points with larger values for D^(k)(p) reside in more sparsely populated clusters, i.e., neighborhoods, of points and are thus more likely to be stronger outliers than points residing in more densely populated neighborhoods which have smaller values for D^(k)(p).

[0009] The distance D^(k)(p) can be measured using any metric or non-metric distance function. The new definition of outliers is not effected by k so long as n is selected to be a small in relation to N. As the value of n increases, so does the time and expense required to identify such outliers since more points must be evaluated. By ranking outliers, the present invention can discriminate between data points p of interest For example, outliers with similar D^(k)(p) distances which are ranked at the bottom of a grouping may not be true outliers and may thus be ignored. As described in more detail below, this new definition also enables the performance of conventional index-based and nested-loop algorithms to be optimized to identify the top n outliers rather than just identifying all of the outliers.

[0010] A key technical tool employed in the present invention for determining which data points are outliers is the approximation of a set of points using their MBR. By computing lower and upper bounds on D^(k)(p) for points in each MBR, MBRs can be identified and eliminated that cannot possibly contain D^(k)(p) outliers. The computation of bounds for MBRs requires that the minimum and maximum distance between a point and an MBR and between two MBRs be defined.

[0011] In determining these upper and lower bounds, the square of the euclidean distance between a point or MBR and another MBR, rather than the euclidean distance itself, is used as the distance metric, thereby requiring fewer and thus less expensive computations. A point p in δ-dimensional space is denoted by [p₁, p₂, . . . , p_(δ)] and a δ-dimensional rectangle R is denoted by the two endpoints of its major diagonal: r=[r₁, r₂, . . . , r_(δ)] and r′=[r′₁, r′₂, . . . , r′δ] such that r_(i)≦r′_(i) for 1≦i≦n. The minimum distance between point p and rectangle R is denoted by MINDIST (p, R) such that every point in R is at a distance of at least MINDIST (p, R) from p. MINDIST (p, R) is defined as:

MINDIST (p, R)=Σ^(δ) _(i=1) x ² _(i), where

[0012] $\begin{matrix} {x_{i} = \left\{ \begin{matrix} {r_{i} - p_{i}} & {{i\quad f\quad p_{i}} < r_{i}} \\ {p_{i} - r_{i}} & {{i\quad f\quad r_{i}^{\prime}} < p_{i}} \\ 0 & {o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad e} \end{matrix} \right.} & (1) \end{matrix}$

[0013] The maximum distance between point p and rectangle R is denoted by MAXDIST (p, R), such that no point in R is at a distance that exceeds MAXDIST (p, R) from point p. MAXDIST (p, R) is defined as:

MAXDIST (p, R)=Σ^(δ) _(i=1) x ² _(i), where

[0014] $\begin{matrix} {x_{i} = \left\{ \begin{matrix} {r_{i}^{\prime} - p_{i}} & {\quad {{i\quad f\quad p_{i}} < {r_{i} + {r_{i}^{\prime}/2}}}} \\ \quad & \quad \\ {p_{i} - r_{i}} & {\quad {o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad e}} \end{matrix} \right.} & (2) \end{matrix}$

[0015] The minimum and maximum distance between two MBRs R and S is defined by the endpoints of their major diagonals, r, r′ and s, s′, respectively. The minimum distance between MBRs R and S is denoted by MINDIST(R, S), wherein every point in R is at a distance of at least MINDIST(R, S) from any point in S and vice-versa. Similarly, the maximum distance between MBRs R and S is denoted by MAXDIST(R, S). MINDIST(R, S) and MAXDIST(R, S) are defined using the following two equations:

MINDIST (R, S)=Σ^(δ) _(i=1) x ² _(i), where

[0016] $\begin{matrix} {x_{i} = \left\{ \begin{matrix} {r_{i} - s_{I}^{\prime}} & {{i\quad f\quad s_{i}^{\prime}} < r_{i}} \\ {p_{i} - r_{i}^{\prime}} & {{i\quad f\quad r_{i}^{\prime}} < p_{i}} \\ 0 & {o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad e} \end{matrix} \right.} & (3) \end{matrix}$

 MINDIST (R, S)=Σ^(δ) _(i=1) x ² _(i), where

x_(i)=max{|s′ _(i) −r _(i)|,|r′_(i) −s _(i)|}  (4)

[0017] The partition-based algorithms used in the present invention eliminate points whose distances from their k^(th) nearest neighbors are so small that they are not in the top n outliers. Such pruning results in substantial improvements in performance speed due to savings in both computation time and required memory compared to conventional methods which calculate D^(k) _(n) for all points p in N.

[0018]FIG. 1 shows a flowchart depicting the steps in an exemplary embodiment of the method for identifying a predetermined number of outliers of interest in a data set according to the present invention. At step 1, the points in a data set are partitioned using a clustering algorithm. At step 2, upper and lower bounds are calculated for the partitions. At step 3, candidate partitions which possibly contain outliers of interest are identified. At step 4, the top n outliers are identified from the points in the candidate partitions. These four steps are implemented using software which is stored on a single computer, or stored on a server on a network. Each of these four steps are described in greater detail below.

[0019] 1. Partition Data Points

[0020] A clustering algorithm is used to partition the data points. Any one of a number of conventional clustering algorithms can be used. Since N can be large, it is desirable to use clustering algorithms that can process large data sets. One such algorithm is the BIRCH algorithm. See Tian Zhang, Raghu Ramakrishnan and Miron Livny, BIRCH: An Efficient Data Clustering Method For Very Large Databases, Proceedings of the ACM SIGMOD Conference on Management of Data, pages 103-114, Montreal, Canada, June 1996. The clustering algorithms then eliminate the partitions that it determines do not contain outliers. To enable such partitions to be effectively eliminated, it is desirable to partition the data set such that points which are close together are assigned to the same partition.

[0021] Since the number of partitions created is usually smaller than the number of points n, pruning the partitions, which is performed at the granularity of partitions rather than points, eliminates a significant number of points as outlier candidates. Consequently, the k^(th) nearest neighbor computations are performed for very few points, thereby speeding up the computation of outliers.

[0022] The main memory size M and the points in the data set are given as inputs to the BIRCH algorithm which generates a set of clusters, i.e. partitions, of generally uniform size that are smaller than and can thus be stored in main memory M. By controlling the memory size M input to BIRCH, the number of partitions generated can be controlled. Each partition is represented by the MBR for its points. The MBRs for partitions may overlap.

[0023] 2. Compute Upper and Lower Bounds for Partitions

[0024] The algorithm computeLowerUpper which is set forth below is used to compute lower and upper bounds, stored in P.lower and P.upper, respectively, on D^(k) for points p in each partition P, wherein for every point pεP, P.lower≦D^(k)(p)≦P.upper.

[0025] Procedure computeLowerUpper (Root, P, k, minD^(k)Dist) begin

[0026]1. nodeList={Root}

[0027]2. P.lower:=P.upper:=∞

[0028]3. lowerHeap:=upperHeap:=Ø

[0029]4. while nodeList is not empty do

[0030]5. delete the first element, Node, from nodeList

[0031]6. if (Node is a leaf){

[0032]7. for each partition Q in Node{

[0033]8. if (MINDIST (P, Q)<P.lower){

[0034]9. lowerHeap.insert (Q)

[0035]10. while lowerHeap.numPoints( )−lowerHeap.top( ).numPoints≧k do

[0036]11. lowerHeap.deleteTop( )

[0037]12. if (lowerHeap.numPoints()>k) P.lower:=MINDIST (P, lowerHeap.top( ))

[0038]13. }

[0039]14. if (MAXDIST (P, Q)<P.upper){

[0040]15. upperHeap.insert (Q)

[0041]16. while upperHeap.numPoints( )—upperHeap.top( ).numPoints≧k do

[0042]17. upperHeap.deleteTop( )

[0043]18. if (upperHeap.numPoints( )≧k) P.lower:=MAXDIST (P, upperHeap.top( ))

[0044]19. if (P.upper≦minD^(k)Dist) return

[0045]20. }

[0046]21. }

[0047]22. }

[0048]23. else}

[0049]24. append Node's children to nodeList

[0050]25. sort nodeList by MINDIST

[0051]26. }

[0052]27. for each Node in nodeList do

[0053]28. if (P.upper≦MAXDIST (P, Node) and P.lower≦MINDIST (P, Node)

[0054]29. delete Node from NodeList

[0055]30. }end.

[0056] The lower and upper bounds for a particular partition P are determined by finding the l partitions closest to P with respect to MINDIST and MAXDIST such that the number of points in P₁, . . , P_(l) is at least k. Since the partitions can be stored in main memory M, a main memory index can be used to find the l partitions closest to P. For each partition, its MBR is stored in the main memory index.

[0057] The algorithm computeCandidatePartitions, which identifies candidate partitions and is set forth and described below, keeps track of minD^(k)Dist which is a lower bound on D^(k) for an outlier, and passes this value to the algorithm computeLowerUpper to optimize the computation of the bounds for a partition P. If P.upper for partition P is less than minD^(k)Dist, then it cannot contain outliers and computation of its bounds is terminated.

[0058] The algorithm computeCandidatePartitions uses three data structures to compute lower and upper bounds: a list nodeList of nodes in the main memory index containing partitions that still need to be searched; and two heaps, each containing the closest partitions to P such that the total number of points in them is at least k. This algorithm stores partitions in two heaps, lowerHeap and upperHeap, in the decreasing order of MINDIST and MAXDIST from P, respectively, such that partitions with the largest values of MINDIST and MAXDIST appear at the top of the heaps.

[0059] 3. Identify Candidate Partitions

[0060] The algorithm computeCandidatePartitions, which is set forth below, is used to identify the candidate partitions that can potentially contain outliers from among the set of all partitions Pset, and to then eliminate the remaining partitions.

[0061] Procedure computeCandidatePartitions (Pset, k, n)

[0062] begin

[0063]1. for each partition P in Pset do

[0064]2. insertIntoIndex (Tree, P)

[0065]3. partHeap:=Ø

[0066]4. minD^(k)Dist=0

[0067]5. for each partition P in Pset do{

[0068]6. compute LowerUpper (Tree.Root, P, k, minD^(k)Dist)

[0069]7. if (P.lower>minD^(k)Dist){

[0070]8. partHeap.insert (P)

[0071]9. while partHeap.num.Points( )—partHeap.top( ).numPoints( )≧n do

[0072]10. partHeap.deleteTop( )

[0073]11. if partHeap.num.Points( )≧n) minD^(k)Dist:=partHeap.top( ).lower

[0074]12. }

[0075]13. }

[0076]14. candSet:=Ø15. for each partition P in Pset do

[0077]16. if (P.upper>minD^(k)Dist){

[0078]17. candSet:=candSet∪{P}

[0079]18. P.neighbors:={Q: QεPset and MINDIST (P, Q)≦P.upper}

[0080]19. }

[0081]20. return candSet end.

[0082] The lower and upper bounds that were previously computed are used to first estimate the lower bound minD^(k)Dist and then a partition P is a candidate only if P.upper≧minD^(k)Dist. The lower bound minD^(k)Dist can be computed using the P.lower values for the partitions, wherein if P₁, . . . , P_(l) represents the partitions with the maximum values for P.lower that contain at least n points, then minD^(k)Dist=min{P_(i).lower:≦i<1}.

[0083] The algorithm computeCandidatePartitions stores the partitions with the largest P.lower values and containing at least n points in the heap partHeap. These partitions are stored in increasing order of P.lower in partHeap and minD^(k)Dist is thus equal to P.lower for the partition P at the top of partHeap. By maintaining minD^(k)Dist, it can be passed as a parameter to computeLowerUpper at step 6 of this algorithm and the computation of bounds for a partition P can be terminated if P.upper for that partition falls below minD^(k)Dist. If, for a partition P, P.lower is greater than the current value of minD^(k)Dist, then P.lower is inserted into partHeap and the value of minD^(k)Dist is appropriately adjusted at steps 8-11 of this algorithm.

[0084] In the for loop at steps 15-19 of this algorithm, the set of candidate partitions candSet is identified, and for each candidate partition P, partitions Q that can potentially contain the k^(th) nearest neighbor for a point in P are added to P neighbors which contain P.

[0085] 4. Identify n Outliers of Interest The top D^(k) _(n) outliers are computed from the points in the candidate partitions candSet. If the points in all the candidate partitions and their neighbors are smaller than main memory M, then all the points are stored in a main memory spatial index. A conventional index-based algorithm is then used to compute the n outliers by probing the index to compute D^(k) values only for points belonging to the candidate partitions. Since both the size of the index and the number of candidate points will typically be smaller than the total number of points in the data set, then the outliers of interest will be identified more quickly by probing the candidate partitions rather than by executing the index-based algorithm for the entire data set of points.

[0086] If all of the candidate partitions and their neighbors exceed the size of main memory M, then the candidate partitions must be processed in batches. In each batch, a subset of the remaining candidate partitions that along with their neighbors are smaller than main memory M is selected for processing. The selection of candidate partitions for a batch is performed by the algorithm genBatchPartitions which is set forth below.

[0087] Procedure genBatchPartitions(candSet, candBatch, neighborBatch)

[0088] begin

[0089]1. candBatch:=neighborBatch:=Ø

[0090]2. delete from candSet partition P with maximum P.lower

[0091]3. candBatch:=candBatch∪P

[0092]4. neighborBatch:=neighborBatch∪P.neighbors

[0093]5. while canSet≠Ø and number of points in neighborBatch≦M do {

[0094]6. delete from candSet partition P for which the number of common points between

[0095]7. P neighbors and neighborBatch is maximum

[0096]8. candBatch:=candBatch∪P

[0097]9. neighborBatch:=neighborBatch∪P.neighbors

[0098]10. }end.

[0099] For each candidate partition P, P.neighbors denotes the neighboring partitions of P, which are all the partitions within distance P.upper from P. Points belonging to neighboring partitions of P are the only points that need to be examined to identify D^(k) for each point in P. The set of candidate partitions is an input parameter to the algorithm while the candidate partitions for a batch and their neighbors are returned in candBatch and neighborBatch, respectively.

[0100] To identify the D^(k) _(n) outliers, the candidate partition with the maximum value for P.lower must first be selected at step 2 of the algorithm genBatchPartitions. This candidate partition is then used to select subsequent partitions at steps 5-10 of the algorithm. For successive partitions, partitions are selected which have a large overlap with the previously selected partitions. The rationale for doing so is that it minimizes the number of batches in which a partition participates. Since processing a partition in a batch requires that all of the points in the batch to be first read from disk and then to be inserted into the main memory index, both of which can be expensive, reducing the number of times these steps are performed for a partition results in substantial performance improvements. In addition, processing partitions with large overlaps in a batch guarantees that the points processed in a single batch are relatively close to each other. As a result, computation of D^(k) for candidate points becomes more efficient. The process of adding candidate partitions to the batch is repeated until the number of points in the selected partitions exceeds M.

[0101] The algorithm computeOutliers which is set forth below is then used to identify the D^(k) _(n) outliers from the candidate partitions in candSet computed by the algorithm computeCandidatePartitions.

[0102] Procedure computeOutliers(candSet, k, n, minD^(k)Dist)

[0103] begin

[0104]1. outheap:=Ø

[0105]2. while candset≠Ø do {

[0106]3. genBatchPartitions(candSet, candbatch, neighborbatch)

[0107]4. for each point p in neighborbatch

[0108]5. insertIntoindex(Tree, p)

[0109]6. while candbatch≠Ø do {

[0110]7. delete from candbatch partition P with maximum P.lower

[0111]8. if (P.upper≧minDkDist)

[0112]9. for each point p in partition P do {

[0113]10. getKthNeighborDist(Tree.Root, p, k, minDkDist)

[0114]11. if (p.DkDist>minDkDist){

[0115]12. outHeap.insert(p)

[0116]13. if (outHeap.numPoints( )>n) outHeap.deleteTop( )

[0117]14. if (outHeap.nurnPoints( )=n) minDkDist:=maxtminDkDist, outHeap.top( ).DkDist}

[0118]15. }

[0119]16. }

[0120]17. }

[0121]18. delete from candset partitions P such that P.upper<minDkDist

[0122]19. }

[0123]20. return outheap end.

[0124] The algorithm computeOutliers uses a conventional index-based algorithm and has as an input parameter the minD^(k)Dist value computed by the algorithm computeCandidatePartitions to identify the candidate partitions. While being executing, the algorithm computeOutliers keeps track of the top n outliers in outHeap and minD^(k)Dist.

[0125] Once the candidate partitions and their neighbors for a batch have been identified at step 3 of the algorithm computeOutliers, the points from neighborBatch are inserted into a main memory index at steps 4 and 5. The while loop spanning steps 6-17 executes the index-based algorithm for points in the candidate partitions. The candidate partitions are evaluated in decreasing order of their P.lower values so that if the points with the highest D^(k) values are found early, then more points can be eliminated later since minD^(k)Dist will converge earlier to the D^(k) value for the nth outlier.

[0126] Since the value of minD^(k)Dist is continuously refined during the procedure, partitions whose P.upper value drops below minD^(k)Dist cannot contain any outliers and can thus be ignored at steps 8 and 18. At Steps 9-16, for every point in p, the procedure getK^(th)NeighborDist is used to compute D^(k)(p) and p is inserted into the outlier heap if it is a stronger outlier than the current nth outlier in outHeap.

[0127] Alternatively, a conventional block nested-loop algorithm can be used in place of an index-based algorithm to compute the to compute the D^(k) _(n) outliers from the candidate partitions in candSet generated by the algorithm computeCandidatePartitions. In a block nested-loop algorithm, if the points in candSet fit in memory, then they are loaded into memory and a single pass is made over their neighboring partitions. Otherwise, genBatchPartitions is used to generate candBatch. The candidate points that are processed during a batch. The only difference is that instead of requiring neighborBatch to fit in main memory, only the c points in candBatch and c heaps are required, one heap for each point p in candBatch containing the distances of p's k nearest neighbors, to fit in memory.

[0128] Points in candBatch and their heaps are stored in memory, and a single pass is made over neighborBatch which is disk-resident. For each point q in neighborBatch, if the distance between a point p in candBatch and q is smaller than p's distance from its current k^(th) nearest neighbor, then q replaces p's k^(th) neighbor. Also, minD^(k)Dist, the lower bound on D^(k) for an outlier, is maintained during execution and used to eliminate points and partitions whose k^(th) nearest neighbor is known to be at a distance of less than minD^(k)Dist as for the index-based algorithm.

[0129] The partition-based algorithms of the present invention scale well with respect to both data set size and data set dimensionality. They also perform more than an order of magnitude better than conventional index-based and nested-loop algorithms.

[0130] Numerous modifications to and alternative embodiments of the present invention will be apparent to those skilled in the art in view of the foregoing description. Accordingly, this description is to be construed as illustrative only and is for the purpose of teaching those skilled in the art the best mode of carrying out the invention. Details of the structure may be varied substantially without departing from the spirit of the invention and the exclusive use of all modifications which come within the scope of the appended claims is reserved. 

What is claimed is:
 1. A method for identifying a predetermined number of data points of interest in a data set, comprising the steps of: partitioning a plurality of data points in a data set into a plurality of partitions; computing lower and upper bounds for each one of the plurality of partitions; identifying a plurality of candidate partitions from the plurality of partitions, wherein each one of the plurality of candidate partitions may include at least one of a predetermined number of data points of interest, wherein the predetermined number of data points of interest are included within the plurality of data points in the data set; and identifying the predetermined number of data points of interest from the plurality of candidate partitions.
 2. The method according to claim 1, wherein the plurality of data points in the data set are partitioned using a clustering algorithm.
 3. The method according to claim 1, wherein given a data set having N data points and a predetermined number of data points of interest n which each have k neighboring data points, a data point p is one of the predetermined number of data points of interest n if no more than an n−1 other points in the data set reside at greater distances from the k neighboring data point than data point p.
 4. The method according to claim 1, wherein for each one of the plurality of partitions the lower and upper bounds are computed by calculating a distance of at least one neighboring data point from the plurality of data points in the partition, the lower bound being the smallest distance from the at least one neighboring data point to a first one of the plurality of data points in the partition and the upper bound being the largest distance from the at least one neighboring data point to a second one of the plurality of data points in the partition.
 5. The method according to claim 4, wherein for the predetermined number of data points of interest, a number of partitions having the largest lower bound values are selected such that the number of data points residing in such partitions is at least equal to the predetermined number of data points of interest, wherein the candidate partitions are comprised of those partitions having upper bound values that are greater than or equal to the smallest lower bound value of the number of partitions and the non-candidate partitions are comprised of those partitions having upper bound values that are less than the smallest lower bound value of the number of partitions, the non-candidate partitions being eliminated from consideration because they do not contain the at least one of the predetermined number of data points of interest.
 6. The method according to claim 1, wherein if the candidate partitions are smaller than a main memory, then all of the data points in the candidate partitions are stored in a main memory spatial index and the predetermined number of points of interest are identified using an index-based algorithm which probes the main memory spatial index.
 7. The method according to claim 1, wherein if the candidate partitions are larger than a main memory, then the partitions are processed in batches such that the overlap between each one of the partitions in a batch is as large as possible so that as many points as possible are processed in each batch.
 8. The method according to claim 7, wherein each one of the batches is comprised of a subset of the plurality of candidate partitions, the subset being smaller than the main memory.
 9. The method according to claim 7, wherein the predetermined number of data points of interest are selected from the batch processed candidate partitions, the predetermined number of data points of interest being those data points residing the farthest from their at least one neighboring data point.
 10. The method according to claim 9, wherein the algorithm computeOutliers uses an index-based algorithm.
 11. The method according to claim 9, wherein the algorithm computeOutliers uses an block nested-loop algorithm.
 12. The method according to claim 1, wherein an MBR is calculated for each one of the plurality of data points in the data set, lower and upper bounds being computed for each one of the data points in the MBR.
 13. The method according to claim 12, wherein the minimum distance between a point p and an MBR R is denoted by MINDIST (p, R) defined as MINDIST (p, R)=Σ^(δ) _(i=1)x² _(i), wherein $x_{i} = \left\{ \begin{matrix} {r_{i} - p_{i}} & {{i\quad f\quad p_{i}} < r_{i}} \\ {p_{i} - r_{i}} & {{i\quad f\quad r_{i}^{\prime}} < p_{i}} \\ 0 & {{o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad e},} \end{matrix} \right.$

and wherein every point in MBR R is at a distance of at least MINDIST (p, R) from point p.
 14. The method according to claim 12, wherein the maximum distance between a point p and an MBR R is denoted by MAXDIST (p, R) defined as MAXDIST (p, R)=Σ^(δ) _(i=1)x² _(i), wherein $x_{i} = \left\{ \begin{matrix} {r_{i}^{\prime} - p_{i}} & {\quad {{i\quad f\quad p_{i}} < {r_{i} + {r_{i}^{\prime}/2}}}} \\ \quad & \quad \\ {p_{i} - r_{i}} & {\quad {{o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad e},}} \end{matrix} \right.$

and wherein no point in MBR R is at a distance that exceeds MAXDIST (p, R) from the point p.
 15. The method according to claim 12, wherein the minimum distance between two MBRs R and S is denoted by MINDIST(R, S) defined as MINDIST (R, S)=Σ^(δ) _(i=1)x² _(i), wherein $x_{i} = \left\{ \begin{matrix} {r_{i} - s_{I}^{\prime}} & {{i\quad f\quad s_{i}^{\prime}} < r_{i}} \\ {p_{i} - r_{i}^{\prime}} & {{i\quad f\quad r_{i}^{\prime}} < p_{i}} \\ 0 & {{o\quad t\quad h\quad e\quad r\quad w\quad i\quad s\quad e},} \end{matrix} \right.$

and wherein every point in MBR R is at a distance of at least MINDIST(R, S) from any point in MBR S.
 16. The method according to claim 12, wherein the maximum distance between two MBRs R and S is denoted by MAXDIST(R, S) defined as MAXDIST (R, S)=Σ^(δ) _(i=1)x² _(i), where x_(i)=max {|s′_(i)−r_(i)|,|r′_(i)−s_(i)|}.
 17. A method for computing the top n outliers in a data set, comprising the steps of: partitioning a plurality of data points in a data set into a plurality of partitions; computing lower and upper bounds for each one of the plurality of partitions; identifying a plurality of candidate partitions from the plurality of partitions, wherein each one of the plurality of candidate partitions may include at least one of n number of outliers of interest, wherein the outliers are included within the plurality of data points in the data set; and identifying the outliers from the plurality of candidate partitions.
 18. The method according to claim 17, wherein given a data set having N data points and a predetermined number of data points of interest n which each have k neighbors, a data point p is one of the n outliers of interest if no more than n−1 other points in the data set have a higher value for D^(k)(p) than data point p.
 19. The method according to claim 3, wherein a data point having a larger value for D^(k)(p) resides in a more sparsely populated neighborhood of points and is thus more likely to be one of the predetermined number of points of interest n than a data point residing in a more densely populated neighborhood having a smaller value for D^(k)(p). 